





"""simple docstring"""





import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


SCREAMING_SNAKE_CASE__   =  {
    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


SCREAMING_SNAKE_CASE__   =  {
    "blocks": "layers",
    "mlp.0": "fc1",
    "mlp.2": "fc2",
    "mlp_ln": "final_layer_norm",
    ".attn.query": ".self_attn.q_proj",
    ".attn.key": ".self_attn.k_proj",
    ".attn.value": ".self_attn.v_proj",
    ".attn_ln": ".self_attn_layer_norm",
    ".attn.out": ".self_attn.out_proj",
    ".cross_attn.query": ".encoder_attn.q_proj",
    ".cross_attn.key": ".encoder_attn.k_proj",
    ".cross_attn.value": ".encoder_attn.v_proj",
    ".cross_attn_ln": ".encoder_attn_layer_norm",
    ".cross_attn.out": ".encoder_attn.out_proj",
    "decoder.ln.": "decoder.layer_norm.",
    "encoder.ln.": "encoder.layer_norm.",
    "token_embedding": "embed_tokens",
    "encoder.positional_embedding": "encoder.embed_positions.weight",
    "decoder.positional_embedding": "decoder.embed_positions.weight",
    "ln_post": "layer_norm",
}


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            lowerCAmelCase    		=			key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        lowerCAmelCase    		=			new_key.replace(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

            print(F'{key} -> {new_key}'     )

            lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
      return s_dict


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      os.makedirs(SCREAMING_SNAKE_CASE				,		exist_ok=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			os.path.basename(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			url.split("""/"""     )[-2]
      lowerCAmelCase    		=			os.path.join(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      if os.path.exists(SCREAMING_SNAKE_CASE     ) and not os.path.isfile(SCREAMING_SNAKE_CASE     ):
            raise RuntimeError(F'{download_target} exists and is not a regular file'     )

      if os.path.isfile(SCREAMING_SNAKE_CASE     ):
            lowerCAmelCase    		=			open(SCREAMING_SNAKE_CASE				,		"""rb"""     ).read()
            if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).hexdigest() == expected_shaaaa:
                  return model_bytes
            else:
                  warnings.warn(F'{download_target} exists, but the SHA256 checksum does not match; re-downloading the file'     )

      with urllib.request.urlopen(SCREAMING_SNAKE_CASE     ) as source, open(SCREAMING_SNAKE_CASE				,		"""wb"""     ) as output:
            with tqdm(
                total=int(source.info().get("""Content-Length"""     )     )				,		ncols=80				,		unit="""iB"""				,		unit_scale=SCREAMING_SNAKE_CASE				,		unit_divisor=10_24     ) as loop:
                  while True:
                        lowerCAmelCase    		=			source.read(81_92     )
                        if not buffer:
                              break

                        output.write(SCREAMING_SNAKE_CASE     )
                        loop.update(len(SCREAMING_SNAKE_CASE     )     )

      lowerCAmelCase    		=			open(SCREAMING_SNAKE_CASE				,		"""rb"""     ).read()
      if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).hexdigest() != expected_shaaaa:
            raise RuntimeError(
                """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."""     )

      return model_bytes


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]     ):







      '''simple docstring'''




      if ".pt" not in checkpoint_path:
            lowerCAmelCase    		=			_download(_MODELS[checkpoint_path]     )
      else:
            lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			original_checkpoint["""dims"""]
      lowerCAmelCase    		=			original_checkpoint["""model_state_dict"""]
      lowerCAmelCase    		=			state_dict["""decoder.token_embedding.weight"""]
      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			True
      lowerCAmelCase    		=			state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

      lowerCAmelCase    		=			WhisperConfig(
          vocab_size=dimensions["""n_vocab"""]				,		encoder_ffn_dim=SCREAMING_SNAKE_CASE				,		decoder_ffn_dim=SCREAMING_SNAKE_CASE				,		num_mel_bins=dimensions["""n_mels"""]				,		d_model=dimensions["""n_audio_state"""]				,		max_target_positions=dimensions["""n_text_ctx"""]				,		encoder_layers=dimensions["""n_audio_layer"""]				,		encoder_attention_heads=dimensions["""n_audio_head"""]				,		decoder_layers=dimensions["""n_text_layer"""]				,		decoder_attention_heads=dimensions["""n_text_state"""]				,		max_source_positions=dimensions["""n_audio_ctx"""]				,		)

      lowerCAmelCase    		=			WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			proj_out_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # # Required parameters
       parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()

       convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)













"""simple docstring"""





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE__   =  300  # TEMPERATURE (unit = K)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		float				,		SCREAMING_SNAKE_CASE	:		float				,		SCREAMING_SNAKE_CASE	:		float				,		):







      '''simple docstring'''




      if donor_conc <= 0:
            raise ValueError("""Donor concentration should be positive"""     )
      elif acceptor_conc <= 0:
            raise ValueError("""Acceptor concentration should be positive"""     )
      elif intrinsic_conc <= 0:
            raise ValueError("""Intrinsic concentration should be positive"""     )
      elif donor_conc <= intrinsic_conc:
            raise ValueError(
                """Donor concentration should be greater than intrinsic concentration"""     )
      elif acceptor_conc <= intrinsic_conc:
            raise ValueError(
                """Acceptor concentration should be greater than intrinsic concentration"""     )
      else:
            return (
                Boltzmann
                * T
                * log((donor_conc * acceptor_conc) / intrinsic_conc**2     )
                / physical_constants["electron volt"][0]
            )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






